Risk prediction models for cardiac rupture after acute myocardial infarction: a systematic review and meta-analysis.

Background: Cardiac rupture (CR) is a catastrophic complication of acute myocardial infarction (AMI), accounting for 10%-20% of AMI-related deaths despite its low incidence. Several risk prediction models have been developed, but their methodological quality and clinical applicability remain uncertain. This study systematically reviewed and quantitatively synthesized existing prediction models for CR post-AMI. Methods: We searched PubMed, Embase, Web of Science, Cochrane Library, CNKI, and Wanfang databases from inception to August 2025. Studies developing or validating risk prediction models for CR after AMI were eligible. Data extraction followed the CHARMS checklist, and methodological quality was assessed with PROBAST. A meta-analysis was performed to pool model discrimination (C-statistic) and evaluate predictors of CR. Subgroup analyses explored heterogeneity by publication period, study design, population, sample size, and validation approach. Results: Ten studies (2017-2024) involving 74-11,603 patients were included. Among them, nine studies reported C-statistics (AUC) along with their confidence intervals (CIs), which were suitable for quantitative synthesis (Meta-analysis). The pooled C-statistic of CR prediction models was 0.83 (95% CI: 0.78-0.89), though with high heterogeneity ( Conclusion: Existing models demonstrate promising discriminatory ability for predicting CR after AMI but are undermined by substantial methodological limitations. Age, sex, Killip grade, LVEF, and PCI status represent robust predictors that should inform future consensus-based models. Large-scale, prospective, and externally validated studies are urgently needed to develop reliable tools for clinical risk stratification and targeted prevention of this lethal complication. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251105703, PROSPERO CRD420251105703.